1. Field of the Invention
The present invention relates to a learning device, a learning method, an identifying device, an identifying method, a program, and an information processing system, and specifically relates to a learning device, a learning method, an identifying device, an identifying method, a program, and an information processing system, which are suitably employed in the event of identifying whether or not a subject existing on an image is a predetermined object to be identified.
2. Description of the Related Art
There has been an identifying method for performing matching employing a template in which an object to be identified is described in a large sense, as an identifying method according to the related art for identifying (recognizing) from an image obtained by a camera an object serving as an object to be identified existing on the image thereof.
With this identifying method, a template in which an object to be identified is described in a large sense, and specifically, a template of the texture of the whole of an object to be identified is prepared beforehand, and matching is performed between the template thereof and an image to be identified (image to be processed).
However, with matching employing a template in which an object to be identified is described, it is difficult to handle partial hiding or distortion of the object to be identified appearing in an image to be processed.
Therefore, an identifying method has been proposed wherein attention is paid to a local region of an image to be processed, feature amount is extracted from each local region is extracted, and combination of the feature amount of each local region (group of the feature amount of a local region), i.e., for example, a vector with the feature amount of each local region as an element, is employed to perform identification.
According to the identifying method employing a group of the feature amount of a local region, a problem such as partial hiding or distortion of an object to be identified, which has been hard to handle by the identifying method employing a template in which an object to be identified is described in a large sense, is eliminated to some extent, and accordingly, high-precision identification can be performed.
The feature amount of a local region is also used for identification of the category of an object in addition to identification of an individual object. For example, an identifying method for identifying a particular category such as a human face using the feature amount of a local region has been proposed (e.g., see P. Viola, M. Jones, Robust Real-time Face Detection, cvpr2001).
Also, with identification of a category, various identifying methods have been proposed. Examples of identifying methods proposed for identifying of a category include an identifying method employing a BoF (Bag of Features) histogram (e.g., see G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoint, ECCV2004), and an identifying method employing correlation of feature amount (e.g., see Japanese Unexamined Patent Application Publication No. 2007-128195).
For example, with the identifying method employing a BoF histogram, representative feature amount called as Visual codebook is employed, thereby suppressing the dimensions of image expression.
However, in the event of employing such Visual codebook, the information of the appearance position of the feature amount in an image region is lost, and accordingly, deterioration in identifying precision may result.
Therefore, in order to deal with such a problem, there has been proposed a method for providing weak position constraint by dividing an image region in a grid (lattice) shape (e.g., see S. Lazebnik, C. Schmid, J. Ponce “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories”, CVPR2006).